Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for bagging is done by making 20 sub-models to depict the accurate one. Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling. Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual algorithms show a moderate bias result. However, the ensemble model gives a better result with R² = 0.911 compared to the decision tree (DT) and gene expression programming (GEP). K-fold cross-validation confirms the model’s accuracy and is done by R², MAE, MSE, and RMSE. Statistical checks reveal that the decision tree with ensemble provides 25%, 121%, and 49% enhancement for errors like MAE, MSE, and RMSE between the target and outcome response.

The experimental design of high‐strength concrete (HSC) requires deep analysis to get the target strength. In this study, machine learning approaches and artificial intelligence python‐based approaches have been utilized to predict the mechanical behaviour of HSC. The data to be used in the modelling consist of several input parameters such as cement, water, fine aggregate, and coarse aggregate in combination with a superplasticizer. Empirical relation with mathematical expression has been proposed using engineering programming. The efficiency of the models is assessed by statistical analysis with the error by using MAE, RRMSE, RSE, and comparisons were made between regression models. Moreover, variable intensity and correlation have shown that deep learning can be used to know the exact amount of materials in civil engineering rather than doing experimental work. The expression tree, as well as normalization of the graph, depicts significant accuracy between target and output values. The results reveal that machine learning proposed adamant accuracy and has elucidated performance in the prediction aspect.

For the production of geopolymer concrete (GPC), fly-ash (FA) like waste material has been effectively utilized by various researchers. In this paper, the soft computing techniques known as gene expression programming (GEP) are executed to deliver an empirical equation to estimate the compressive strength $f_c^{\prime }$ of GPC made by employing FA. To build a model, a consistent, extensive and reliable data base is compiled through a detailed review of the published research. The compiled data set is comprised of 298 $f_c^{\prime }$ experimental results. The utmost dominant parameters are counted as explanatory variables, in other words, the extra water added as percent FA ($\%E_W$), the percentage of plasticizer ($\%P$), the initial curing temperature ($T$), the age of the specimen ($A$), the curing duration ($t$), the fine aggregate to total aggregate ratio ($F/A_G$), the percentage of total aggregate by volume ($\%A_G$), the percent SiO₂ solids to water ratio ($\% S/W$) in sodium silicate (Na₂SiO₃) solution, the NaOH solution molarity ($M$), the activator or alkali to FA ratio ($A_L/F_A$), the sodium oxide (Na₂O) to water ratio ($N/W$) for preparing Na₂SiO₃ solution, and the Na₂SiO₃ to NaOH ratio ($N_s/N_o$). A GEP empirical equation is proposed to estimate the $f_c^{\prime }$ of GPC made with FA. The accuracy, generalization, and prediction capability of the proposed model was evaluated by performing parametric analysis, applying statistical checks, and then compared with non-linear and linear regression equations.